Automatic identification of objects by an optronic system is used in multiple fields. The field of defence and security for the recognition of targets, the medical field for the detection of subcutaneous and cutaneous tumours or the field of micro-electronics for the observation of hardware components during their manufacture may be cited by way of example. An optronic system creates a 3D image of an object present in a complex optronic scene. This image must for example make it possible to pick out camouflaged targets behind camouflage netting, under trees, etc. This operational condition constitutes the major problem.
In this field of 3D imaging, it is necessary to obtain a set of measurements of the object to be reconstructed dependent on a variable parameter (angle and/or dimension in terms of depth for example); this data set makes it possible to reconstruct the volume by applying inverse reconstruction techniques. From a mathematical point of view, the technique breaks down into two steps: direct measurement, optionally processed using a model of the physical phenomena measured, and then reconstruction by inversion on the basis of these direct measurements. The first problem consists in providing in a reasonable time a set of direct data that can be utilized for the inversion process. This problem naturally covers all the technological problems (high rate illumination laser, short pulse, fast detector block, pointing). The second problem relates to the inversion procedure used and its mathematical implementation.
A first optronic system for identifying objects is known. It involves a profilometric 3D active imaging system whose characteristics are disclosed in a publication whose references are as follows: “Lincoln laboratory Journal Vol. 15 number 1 2005, Jigsaw: a foliage Penetrating 3D imaging laser radar system”. U.S. patent publication US/2008/0181487 presenting the spatial registration procedure for aerial craft is known. It involves an air/ground acquisition system which performs a certain number of measurements on an optronic scene from different observation angles. For each observation angle, the system recovers distance and intensity information on a grid of large dimension (>256×256). The system uses the principle of profilometry to pick out the various points from echoes situated on one and the same sighting line. The principle of this 3D imaging relies on the use of a short-pulse (of the order of a nanosecond) laser source with a high sampling rate. A single laser pulse illuminates the complete scene; a 2D matrix detector counts the photons backscattered by the scene and their delay with respect to emission. The image is produced by scanning. This technique then requires optimization of the scan to produce an image in a time compatible with the displacement of the UAV (“Unmanned Aerial Vehicles”) for example. The image produced by this device is a map of photons detected in 3D by virtue of the multiple echoes on a sighting line. The accumulation of several maps of this type for different observation angles after spatial registration makes it possible to create clouds of points portraying the surface of the object to be identified. The problem of the inverse function of constructing the 3D image is limited to concatenating a set of data in the same reference frame and to extracting the zone of interest containing the data to be identified. This system exhibits several technological difficulties. First of all, it is necessary to use a short-pulse laser and a detection system provided with fast electronics to determine the distance between the laser source and the detector for each of the pixels. Moreover, the step of registering the clouds of points requires the use of an efficacious pointing and geo-location system so as to allow the concatenation of images in one and the same reference frame. To summarize, such a solution exhibits a significant cost related to the technology to be implemented for image acquisition and therefore does not make it possible to democratize a “full-3D” detection application such as this in all fields. Furthermore, this solution remains difficult to implement for guided airborne systems.
A second optronic system of transmission 3D tomographic imaging used in medical imaging is known. The general principle is as follows: a fine pencil of X rays, issuing from a collimated source, scans the body of the patient and carries out a first profile of the object. The system then undergoes an elementary rotation and the process is repeated, thus providing new profiles, stemming from a different projection angle. On the basis of these data and by virtue of the algorithms based on Radon's theory, the values of the attenuation coefficients at each point of the section must be computed, thus providing a mapping of the internal tissues. The scanner therefore relies on the greater or lesser absorption of X rays, depending on the medium traversed. The direct problem in this case relies on knowing the electromagnetic absorption parameters for the tissues traversed.
A third known solution is a system for modelling 3D objects on the basis of multiple views. Two main principal methods of synthetic construction of objects according to this principle are known.
The first method consists in extracting silhouettes. The idea is to place the object to be represented in three dimensions on a turntable and to capture snapshots by conventional visible imaging from various angles. After extracting the silhouette of the object on each image, each silhouette is applied over a 3D volume according to the observation angle, preserving only the part of the volume which is situated inside the silhouette. This method exhibits a problem related to the lighting of the object which must comprise the fewest possible shadows and the object must stand out perfectly from the background whatever the angle. By this means a not very detailed exterior envelope of the object is obtained and if the object contains zones of shadow or noise a large part of the information is lost. Moreover, this method does not allow identification of a partially masked object.
The second method is the procedure for minimizing surface areas. This technique consists in reconstructing a surface on the basis of noteworthy points on the 2D images of objects by algorithms for minimizing the surface area or for linking up small patches. The technique consisting in applying patches over a grid of telemetred points is generally performed from a single observation angle; it may be extended to several angles within the framework of a complete reconstruction entailing significant time and means of computation but may not be applied to partially masked objects on account of the necessary continuity between the patches. These procedures are akin to stereoscopy techniques making it possible to reproduce the perception of relief on the basis of two plane images (2.5D image, depth perception).
Ultimately, the problems with the prior art solutions are on the one hand the cost and the inaccessibility of the technological means and on the other hand the impossibility of being used in unknown and uncontrolled environments or of allowing the detection of camouflaged objects.